AI Vehicle Monitoring System for Malware Detection
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Solution Overview
Problem
Vehicles are vulnerable to malware, which can compromise safety systems and cause unintended operations, posing risks to persons and property, and existing security measures are insufficient to prevent all malicious or undesirable software and hardware modifications.
Innovation Solution
A vehicle monitoring and analysis system utilizing a machine learning system and artificial intelligence to detect and mitigate unauthorized hardware and software modifications, including the use of a sandbox environment for untrusted components and alert systems for potential threats, allowing for proactive monitoring and reporting of malware presence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If vehicle hardware and software are updated to improve functionality and performance, then vehicle capabilities are enhanced, but vulnerability to malware increases
Solution Approach 1:
The system performs preliminary validation of hardware and software before allowing installation. A monitoring module captures data about the vehicle's baseline state, and any attempted modifications are analyzed against this baseline before execution, preventing malicious code from establishing itself
Solution Approach 2:
An intermediary monitoring system is introduced between the update mechanism and the vehicle's control systems. This intermediary captures and analyzes data about attempted modifications, acting as a security layer that can block or alert on suspicious activities without preventing legitimate updates
2Reliability
If debugging and testing processes are employed to account for known vulnerabilities, then security against known malware is improved, but inability to detect unknown malware persists
Solution Approach 1:
The monitoring system continuously captures data about vehicle system behavior and provides feedback to the analysis module. This feedback loop enables the system to learn from observed patterns and detect anomalies that deviate from normal operation, allowing identification of both known and unknown malware based on behavioral changes rather than signature matching
Solution Approach 2:
The system performs self-monitoring and self-diagnosis by continuously analyzing its own operational data. The monitoring module captures data about system states and the analysis module independently evaluates this data for signs of compromise, enabling the vehicle to detect unknown threats without external intervention
3Reliability
If malware detection and monitoring systems are implemented, then security against malware is improved, but system complexity increases
Solution Approach 1:
The monitoring module serves multiple functions: it captures data about system states, validates attempted modifications, detects anomalies, and provides alerts. By consolidating these security functions into a single multi-functional module rather than separate specialized components, the system achieves comprehensive protection without proportionally increasing complexity
Solution Approach 2:
The system uses the vehicle's existing operational data and control structures for monitoring purposes. Rather than requiring entirely separate sensing and analysis hardware, the system leverages data already being collected by the vehicle's normal operational systems, reducing the need for additional dedicated monitoring components
Data Source
AI summary
An Artificial Intelligence (AI) interface and engine is described that enables the monitoring and analysis of vehicle information to determine if the vehicle has had at least one of hardware and software maliciously changed, added, or removed. The AI interface may determine the presence of the maliciously changed, added, or removed hardware and/or software such as by receiving an emergency condition from at least one sensor that is in disagreement with another sensor.